Article
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Preserved in Portico This version is not peer-reviewed
PDF Malware Detection Based on Optimizable Decision Trees
Version 1
: Received: 6 September 2022 / Approved: 7 September 2022 / Online: 7 September 2022 (05:33:40 CEST)
A peer-reviewed article of this Preprint also exists.
Abu Al-Haija, Q.; Odeh, A.; Qattous, H. PDF Malware Detection Based on Optimizable Decision Trees. Electronics 2022, 11, 3142. Abu Al-Haija, Q.; Odeh, A.; Qattous, H. PDF Malware Detection Based on Optimizable Decision Trees. Electronics 2022, 11, 3142.
Abstract
Portable Document Format (PDF) files are one of the most universally used file types. This has fascinated hackers to develop methods to use these normally innocent PDF files to create security threats via infection vectors PDF files. This is usually realized by hiding embedded malicious code in the victims’ PDF documents to infect their machines. This, of course, results in PDF Malware and requires techniques to identify benign files from malicious files. Research studies indicated that machine-learning methods provide efficient detection techniques against such malware. In this paper, we present a new detection system that can analyze PDF documents in order to identify benign PFD files from malware PFD files. The proposed system makes use of the AdaBoost decision tree with optimal hyperparameters, which is trained and evaluated on a modern-inclusive dataset, viz. Evasive-PDFMal2022. The investigational assessment demonstrates a lightweight-accurate PDF detection system, achieving a 98.84% prediction accuracy with a short prediction interval of 2.174 μSec. To this end, the proposed model outperforms other state-of-the-art models in the same study area. Hence, the proposed system can be effectively utilized to uncover PDF malware at high detection performance and low detection overhead.
Keywords
Portable Document Format (PDF); machine learning; detection; optimizable decision tree; Ada-Boost; PDF malware; evasion attacks; cybersecurity
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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